Congestive Heart Failure Detection Via Short-Time Electrocardiographic Monitoring For Fast Reference Advice In Urgent Medical Conditions

Abstract

This study proposed the detection approach for the Congestive Heart Failure (CHF) disease by short-time electrocardiographic monitoring. Recent literature reviews only reported that RR intervals and Heart Rate Variability (HRV) indicate key hidden information to discriminate CHF groups from healthy controls. However what if possible to find certain short-time electrocardiographic monitoring duration to give fast reference advice for CHF diagnoses, has not been well addressed. In this study, commonly applied databases from Phy-sioNet are introduced, in which approximate 20-hour individual Electrocardiogram (ECG) recordings are used. Those signals are first classified into largely variable assessment lengths.

Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

“…The results of our pilot study at least highlight that we should not treat machine learning and data science as crystal balls for making predictions and automating decisionmaking; we should rather use these techniques to more critically examine all available information and enhance existing human expertise. The study complied with French law for observational studies, was approved by the ethics committee of the French Intensive Care Society (CE SRLF [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28], was approved by the Commission Nationale de l'Informatique et des Libertés (CNIL) for The treatment of personal health data. We gave written and oral information to patients or next-of-kin.…”

Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

“…In parallel, we designed a data-driven approach. Different AI methods were available; we selected the Random Forest method because it is one of the most efficient strategies for providing a predictive algorithm in this context [18][19][20][21]. Importantly, the final algorithm was tasked with providing predictions for a novel population independent of the dataset used for the algorithm construction.…”

“…Step 1: patient data collection Prospective data collection was conducted in a single center over an 18-month period. The study complied with French law for observational studies, was approved by the ethics committee of the French Intensive Care Society (CE SRLF [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28], was approved by the Commission Nationale de l'Informatique et des Libertés (CNIL) for the treatment of personal health data. We gave written and oral information to patients or next-of-kin.…”

Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

“…In parallel, we designed a data-driven approach. Different AI methods were available; we selected the Random Forest method because it is one of the most efficient strategies for providing a predictive algorithm in this context [17][18][19][20]. Importantly, the final algorithm was tasked with providing predictions for a novel population independent of the dataset used for the algorithm construction.…”

“…Prospective data collection was conducted in a single center over an 18-month period. The study complied with French law for observational studies, was approved by the ethics committee of the French Intensive Care Society (CE SRLF [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28], was approved by the Commission Nationale de l'Informatique et des Libertés (CNIL) for the treatment of personal health data. We gave written and oral information to patients or next-of-kin.…”